Technologies for providing accelerated functions as a service in a disaggregated architecture

ABSTRACT

Technologies for providing accelerated functions as a service in a disaggregated architecture include a compute device that is to receive a request for an accelerated task. The task is associated with a kernel usable by an accelerator sled communicatively coupled to the compute device to execute the task. The compute device is further to determine, in response to the request and with a database indicative of kernels and associated accelerator sleds, an accelerator sled that includes an accelerator device configured with the kernel associated with the request. Additionally, the compute device is to assign the task to the determined accelerator sled for execution. Other embodiments are also described and claimed.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. Provisional PatentApplication No. 62/427,268, filed Nov. 29, 2016, and Indian ProvisionalPatent Application No. 201741030632, filed Aug. 30, 2017.

BACKGROUND

Typically, in data centers in which workloads (e.g., applications) areassigned to compute devices for execution on behalf of a customer (e.g.,in a cloud data center), an accelerator device, if any, is local to(e.g., on the same board) as a general purpose processor assigned toexecute a workload and is capable of providing only a fixed type ofacceleration. As such, if the particular application executed by thegeneral purpose processor does not include functions or operations(e.g., tasks) that can take advantage of the acceleration capabilitiesof the local accelerator device, then the application is executed at anun-accelerated speed and the local accelerator device goes unused duringthe execution of the application, resulting in wasted resources.

BRIEF DESCRIPTION OF THE DRAWINGS

The concepts described herein are illustrated by way of example and notby way of limitation in the accompanying figures. For simplicity andclarity of illustration, elements illustrated in the figures are notnecessarily drawn to scale. Where considered appropriate, referencelabels have been repeated among the figures to indicate corresponding oranalogous elements.

FIG. 1 is a diagram of a conceptual overview of a data center in whichone or more techniques described herein may be implemented according tovarious embodiments;

FIG. 2 is a diagram of an example embodiment of a logical configurationof a rack of the data center of FIG. 1;

FIG. 3 is a diagram of an example embodiment of another data center inwhich one or more techniques described herein may be implementedaccording to various embodiments;

FIG. 4 is a diagram of another example embodiment of a data center inwhich one or more techniques described herein may be implementedaccording to various embodiments;

FIG. 5 is a diagram of a connectivity scheme representative oflink-layer connectivity that may be established among various sleds ofthe data centers of FIGS. 1, 3, and 4;

FIG. 6 is a diagram of a rack architecture that may be representative ofan architecture of any particular one of the racks depicted in FIGS. 1-4according to some embodiments;

FIG. 7 is a diagram of an example embodiment of a sled that may be usedwith the rack architecture of FIG. 6;

FIG. 8 is a diagram of an example embodiment of a rack architecture toprovide support for sleds featuring expansion capabilities;

FIG. 9 is a diagram of an example embodiment of a rack implementedaccording to the rack architecture of FIG. 8;

FIG. 10 is a diagram of an example embodiment of a sled designed for usein conjunction with the rack of FIG. 9;

FIG. 11 is a diagram of an example embodiment of a data center in whichone or more techniques described herein may be implemented according tovarious embodiments;

FIG. 12 is a simplified block diagram of at least one embodiment of asystem for providing accelerated functions as a service;

FIG. 13 is a simplified block diagram of at least one embodiment of anorchestrator server of the system of FIG. 12;

FIG. 14 is a simplified block diagram of at least one embodiment of anenvironment that may be established by the orchestrator server of FIGS.12 and 13;

FIGS. 15-17 are a simplified flow diagram of at least one embodiment ofa method for providing accelerated functions as a service that may beperformed by the orchestrator server of FIGS. 12 and 13; and

FIG. 18 is a simplified block diagram of types of information that maybe indicated in metadata associated with a task that is to beaccelerated with the system of FIG. 12.

DETAILED DESCRIPTION OF THE DRAWINGS

While the concepts of the present disclosure are susceptible to variousmodifications and alternative forms, specific embodiments thereof havebeen shown by way of example in the drawings and will be describedherein in detail. It should be understood, however, that there is nointent to limit the concepts of the present disclosure to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives consistent with the presentdisclosure and the appended claims.

References in the specification to “one embodiment,” “an embodiment,”“an illustrative embodiment,” etc., indicate that the embodimentdescribed may include a particular feature, structure, orcharacteristic, but every embodiment may or may not necessarily includethat particular feature, structure, or characteristic. Moreover, suchphrases are not necessarily referring to the same embodiment. Further,when a particular feature, structure, or characteristic is described inconnection with an embodiment, it is submitted that it is within theknowledge of one skilled in the art to effect such feature, structure,or characteristic in connection with other embodiments whether or notexplicitly described. Additionally, it should be appreciated that itemsincluded in a list in the form of “at least one A, B, and C” can mean(A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).Similarly, items listed in the form of “at least one of A, B, or C” canmean (A); (B); (C); (A and B); (A and C); (B and C); or (A, B, and C).

The disclosed embodiments may be implemented, in some cases, inhardware, firmware, software, or any combination thereof. The disclosedembodiments may also be implemented as instructions carried by or storedon a transitory or non-transitory machine-readable (e.g.,computer-readable) storage medium, which may be read and executed by oneor more processors. A machine-readable storage medium may be embodied asany storage device, mechanism, or other physical structure for storingor transmitting information in a form readable by a machine (e.g., avolatile or non-volatile memory, a media disc, or other media device).

In the drawings, some structural or method features may be shown inspecific arrangements and/or orderings. However, it should beappreciated that such specific arrangements and/or orderings may not berequired. Rather, in some embodiments, such features may be arranged ina different manner and/or order than shown in the illustrative figures.Additionally, the inclusion of a structural or method feature in aparticular figure is not meant to imply that such feature is required inall embodiments and, in some embodiments, may not be included or may becombined with other features.

FIG. 1 illustrates a conceptual overview of a data center 100 that maygenerally be representative of a data center or other type of computingnetwork in/for which one or more techniques described herein may beimplemented according to various embodiments. As shown in FIG. 1, datacenter 100 may generally contain a plurality of racks, each of which mayhouse computing equipment comprising a respective set of physicalresources. In the particular non-limiting example depicted in FIG. 1,data center 100 contains four racks 102A to 102D, which house computingequipment comprising respective sets of physical resources (PCRs) 105Ato 105D. According to this example, a collective set of physicalresources 106 of data center 100 includes the various sets of physicalresources 105A to 105D that are distributed among racks 102A to 102D.Physical resources 106 may include resources of multiple types, suchas—for example—processors, co-processors, accelerators, fieldprogrammable gate arrays (FPGAs), memory, and storage. The embodimentsare not limited to these examples.

The illustrative data center 100 differs from typical data centers inmany ways. For example, in the illustrative embodiment, the circuitboards (“sleds”) on which components such as CPUs, memory, and othercomponents are placed are designed for increased thermal performance Inparticular, in the illustrative embodiment, the sleds are shallower thantypical boards. In other words, the sleds are shorter from the front tothe back, where cooling fans are located. This decreases the length ofthe path that air must to travel across the components on the board.Further, the components on the sled are spaced further apart than intypical circuit boards, and the components are arranged to reduce oreliminate shadowing (i.e., one component in the air flow path of anothercomponent). In the illustrative embodiment, processing components suchas the processors are located on a top side of a sled while near memory,such as DIMMs, are located on a bottom side of the sled. As a result ofthe enhanced airflow provided by this design, the components may operateat higher frequencies and power levels than in typical systems, therebyincreasing performance. Furthermore, the sleds are configured to blindlymate with power and data communication cables in each rack 102A, 102B,102C, 102D, enhancing their ability to be quickly removed, upgraded,reinstalled, and/or replaced. Similarly, individual components locatedon the sleds, such as processors, accelerators, memory, and data storagedrives, are configured to be easily upgraded due to their increasedspacing from each other. In the illustrative embodiment, the componentsadditionally include hardware attestation features to prove theirauthenticity.

Furthermore, in the illustrative embodiment, the data center 100utilizes a single network architecture (“fabric”) that supports multipleother network architectures including Ethernet and Omni-Path. The sleds,in the illustrative embodiment, are coupled to switches via opticalfibers, which provide higher bandwidth and lower latency than typicaltwisted pair cabling (e.g., Category 5, Category 5e, Category 6, etc.).Due to the high bandwidth, low latency interconnections and networkarchitecture, the data center 100 may, in use, pool resources, such asmemory, accelerators (e.g., graphics accelerators, FPGAs, ASICs, etc.),and data storage drives that are physically disaggregated, and providethem to compute resources (e.g., processors) on an as needed basis,enabling the compute resources to access the pooled resources as if theywere local. The illustrative data center 100 additionally receivesutilization information for the various resources, predicts resourceutilization for different types of workloads based on past resourceutilization, and dynamically reallocates the resources based on thisinformation.

The racks 102A, 102B, 102C, 102D of the data center 100 may includephysical design features that facilitate the automation of a variety oftypes of maintenance tasks. For example, data center 100 may beimplemented using racks that are designed to be robotically-accessed,and to accept and house robotically-manipulatable resource sleds.Furthermore, in the illustrative embodiment, the racks 102A, 102B, 102C,102D include integrated power sources that receive a greater voltagethan is typical for power sources. The increased voltage enables thepower sources to provide additional power to the components on eachsled, enabling the components to operate at higher than typicalfrequencies.

FIG. 2 illustrates an exemplary logical configuration of a rack 202 ofthe data center 100. As shown in FIG. 2, rack 202 may generally house aplurality of sleds, each of which may comprise a respective set ofphysical resources. In the particular non-limiting example depicted inFIG. 2, rack 202 houses sleds 204-1 to 204-4 comprising respective setsof physical resources 205-1 to 205-4, each of which constitutes aportion of the collective set of physical resources 206 comprised inrack 202. With respect to FIG. 1, if rack 202 is representative of—forexample—rack 102A, then physical resources 206 may correspond to thephysical resources 105A comprised in rack 102A. In the context of thisexample, physical resources 105A may thus be made up of the respectivesets of physical resources, including physical storage resources 205-1,physical accelerator resources 205-2, physical memory resources 205-3,and physical compute resources 205-5 comprised in the sleds 204-1 to204-4 of rack 202. The embodiments are not limited to this example. Eachsled may contain a pool of each of the various types of physicalresources (e.g., compute, memory, accelerator, storage). By havingrobotically accessible and robotically manipulatable sleds comprisingdisaggregated resources, each type of resource can be upgradedindependently of each other and at their own optimized refresh rate.

FIG. 3 illustrates an example of a data center 300 that may generally berepresentative of one in/for which one or more techniques describedherein may be implemented according to various embodiments. In theparticular non-limiting example depicted in FIG. 3, data center 300comprises racks 302-1 to 302-32. In various embodiments, the racks ofdata center 300 may be arranged in such fashion as to define and/oraccommodate various access pathways. For example, as shown in FIG. 3,the racks of data center 300 may be arranged in such fashion as todefine and/or accommodate access pathways 311A, 311B, 311C, and 311D. Insome embodiments, the presence of such access pathways may generallyenable automated maintenance equipment, such as robotic maintenanceequipment, to physically access the computing equipment housed in thevarious racks of data center 300 and perform automated maintenance tasks(e.g., replace a failed sled, upgrade a sled). In various embodiments,the dimensions of access pathways 311A, 311B, 311C, and 311D, thedimensions of racks 302-1 to 302-32, and/or one or more other aspects ofthe physical layout of data center 300 may be selected to facilitatesuch automated operations. The embodiments are not limited in thiscontext.

FIG. 4 illustrates an example of a data center 400 that may generally berepresentative of one in/for which one or more techniques describedherein may be implemented according to various embodiments. As shown inFIG. 4, data center 400 may feature an optical fabric 412. Opticalfabric 412 may generally comprise a combination of optical signalingmedia (such as optical cabling) and optical switching infrastructure viawhich any particular sled in data center 400 can send signals to (andreceive signals from) each of the other sleds in data center 400. Thesignaling connectivity that optical fabric 412 provides to any givensled may include connectivity both to other sleds in a same rack andsleds in other racks. In the particular non-limiting example depicted inFIG. 4, data center 400 includes four racks 402A to 402D. Racks 402A to402D house respective pairs of sleds 404A-1 and 404A-2, 404B-1 and404B-2, 404C-1 and 404C-2, and 404D-1 and 404D-2. Thus, in this example,data center 400 comprises a total of eight sleds. Via optical fabric412, each such sled may possess signaling connectivity with each of theseven other sleds in data center 400. For example, via optical fabric412, sled 404A-1 in rack 402A may possess signaling connectivity withsled 404A-2 in rack 402A, as well as the six other sleds 404B-1, 404B-2,404C-1, 404C-2, 404D-1, and 404D-2 that are distributed among the otherracks 402B, 402C, and 402D of data center 400. The embodiments are notlimited to this example.

FIG. 5 illustrates an overview of a connectivity scheme 500 that maygenerally be representative of link-layer connectivity that may beestablished in some embodiments among the various sleds of a datacenter, such as any of example data centers 100, 300, and 400 of FIGS.1, 3, and 4. Connectivity scheme 500 may be implemented using an opticalfabric that features a dual-mode optical switching infrastructure 514.Dual-mode optical switching infrastructure 514 may generally comprise aswitching infrastructure that is capable of receiving communicationsaccording to multiple link-layer protocols via a same unified set ofoptical signaling media, and properly switching such communications. Invarious embodiments, dual-mode optical switching infrastructure 514 maybe implemented using one or more dual-mode optical switches 515. Invarious embodiments, dual-mode optical switches 515 may generallycomprise high-radix switches. In some embodiments, dual-mode opticalswitches 515 may comprise multi-ply switches, such as four-ply switches.In various embodiments, dual-mode optical switches 515 may featureintegrated silicon photonics that enable them to switch communicationswith significantly reduced latency in comparison to conventionalswitching devices. In some embodiments, dual-mode optical switches 515may constitute leaf switches 530 in a leaf-spine architectureadditionally including one or more dual-mode optical spine switches 520.

In various embodiments, dual-mode optical switches may be capable ofreceiving both Ethernet protocol communications carrying InternetProtocol (IP packets) and communications according to a second,high-performance computing (HPC) link-layer protocol (e.g., Intel'sOmni-Path Architecture's, Infiniband) via optical signaling media of anoptical fabric. As reflected in FIG. 5, with respect to any particularpair of sleds 504A and 504B possessing optical signaling connectivity tothe optical fabric, connectivity scheme 500 may thus provide support forlink-layer connectivity via both Ethernet links and HPC links. Thus,both Ethernet and HPC communications can be supported by a singlehigh-bandwidth, low-latency switch fabric. The embodiments are notlimited to this example.

FIG. 6 illustrates a general overview of a rack architecture 600 thatmay be representative of an architecture of any particular one of theracks depicted in FIGS. 1 to 4 according to some embodiments. Asreflected in FIG. 6, rack architecture 600 may generally feature aplurality of sled spaces into which sleds may be inserted, each of whichmay be robotically-accessible via a rack access region 601. In theparticular non-limiting example depicted in FIG. 6, rack architecture600 features five sled spaces 603-1 to 603-5. Sled spaces 603-1 to 603-5feature respective multi-purpose connector modules (MPCMs) 616-1 to616-5.

FIG. 7 illustrates an example of a sled 704 that may be representativeof a sled of such a type. As shown in FIG. 7, sled 704 may comprise aset of physical resources 705, as well as an MPCM 716 designed to couplewith a counterpart MPCM when sled 704 is inserted into a sled space suchas any of sled spaces 603-1 to 603-5 of FIG. 6. Sled 704 may alsofeature an expansion connector 717. Expansion connector 717 maygenerally comprise a socket, slot, or other type of connection elementthat is capable of accepting one or more types of expansion modules,such as an expansion sled 718. By coupling with a counterpart connectoron expansion sled 718, expansion connector 717 may provide physicalresources 705 with access to supplemental computing resources 705Bresiding on expansion sled 718. The embodiments are not limited in thiscontext.

FIG. 8 illustrates an example of a rack architecture 800 that may berepresentative of a rack architecture that may be implemented in orderto provide support for sleds featuring expansion capabilities, such assled 704 of FIG. 7. In the particular non-limiting example depicted inFIG. 8, rack architecture 800 includes seven sled spaces 803-1 to 803-7,which feature respective MPCMs 816-1 to 816-7. Sled spaces 803-1 to803-7 include respective primary regions 803-1A to 803-7A and respectiveexpansion regions 803-1B to 803-7B. With respect to each such sledspace, when the corresponding MPCM is coupled with a counterpart MPCM ofan inserted sled, the primary region may generally constitute a regionof the sled space that physically accommodates the inserted sled. Theexpansion region may generally constitute a region of the sled spacethat can physically accommodate an expansion module, such as expansionsled 718 of FIG. 7, in the event that the inserted sled is configuredwith such a module.

FIG. 9 illustrates an example of a rack 902 that may be representativeof a rack implemented according to rack architecture 800 of FIG. 8according to some embodiments. In the particular non-limiting exampledepicted in FIG. 9, rack 902 features seven sled spaces 903-1 to 903-7,which include respective primary regions 903-1A to 903-7A and respectiveexpansion regions 903-1B to 903-7B. In various embodiments, temperaturecontrol in rack 902 may be implemented using an air cooling system. Forexample, as reflected in FIG. 9, rack 902 may feature a plurality offans 919 that are generally arranged to provide air cooling within thevarious sled spaces 903-1 to 903-7. In some embodiments, the height ofthe sled space is greater than the conventional “1U” server height. Insuch embodiments, fans 919 may generally comprise relatively slow, largediameter cooling fans as compared to fans used in conventional rackconfigurations. Running larger diameter cooling fans at lower speeds mayincrease fan lifetime relative to smaller diameter cooling fans runningat higher speeds while still providing the same amount of cooling. Thesleds are physically shallower than conventional rack dimensions.Further, components are arranged on each sled to reduce thermalshadowing (i.e., not arranged serially in the direction of air flow). Asa result, the wider, shallower sleds allow for an increase in deviceperformance because the devices can be operated at a higher thermalenvelope (e.g., 250W) due to improved cooling (i.e., no thermalshadowing, more space between devices, more room for larger heat sinks,etc.).

MPCMs 916-1 to 916-7 may be configured to provide inserted sleds withaccess to power sourced by respective power modules 920-1 to 920-7, eachof which may draw power from an external power source 921. In variousembodiments, external power source 921 may deliver alternating current(AC) power to rack 902, and power modules 920-1 to 920-7 may beconfigured to convert such AC power to direct current (DC) power to besourced to inserted sleds. In some embodiments, for example, powermodules 920-1 to 920-7 may be configured to convert 277-volt AC powerinto 12-volt DC power for provision to inserted sleds via respectiveMPCMs 916-1 to 916-7. The embodiments are not limited to this example.

MPCMs 916-1 to 916-7 may also be arranged to provide inserted sleds withoptical signaling connectivity to a dual-mode optical switchinginfrastructure 914, which may be the same as—or similar to—dual-modeoptical switching infrastructure 514 of FIG. 5. In various embodiments,optical connectors contained in MPCMs 916-1 to 916-7 may be designed tocouple with counterpart optical connectors contained in MPCMs ofinserted sleds to provide such sleds with optical signaling connectivityto dual-mode optical switching infrastructure 914 via respective lengthsof optical cabling 922-1 to 922-7. In some embodiments, each such lengthof optical cabling may extend from its corresponding MPCM to an opticalinterconnect loom 923 that is external to the sled spaces of rack 902.In various embodiments, optical interconnect loom 923 may be arranged topass through a support post or other type of load-bearing element ofrack 902. The embodiments are not limited in this context. Becauseinserted sleds connect to an optical switching infrastructure via MPCMs,the resources typically spent in manually configuring the rack cablingto accommodate a newly inserted sled can be saved.

FIG. 10 illustrates an example of a sled 1004 that may be representativeof a sled designed for use in conjunction with rack 902 of FIG. 9according to some embodiments. Sled 1004 may feature an MPCM 1016 thatcomprises an optical connector 1016A and a power connector 1016B, andthat is designed to couple with a counterpart MPCM of a sled space inconjunction with insertion of MPCM 1016 into that sled space. CouplingMPCM 1016 with such a counterpart MPCM may cause power connector 1016 tocouple with a power connector comprised in the counterpart MPCM. Thismay generally enable physical resources 1005 of sled 1004 to sourcepower from an external source, via power connector 1016 and powertransmission media 1024 that conductively couples power connector 1016to physical resources 1005.

Sled 1004 may also include dual-mode optical network interface circuitry1026. Dual-mode optical network interface circuitry 1026 may generallycomprise circuitry that is capable of communicating over opticalsignaling media according to each of multiple link-layer protocolssupported by dual-mode optical switching infrastructure 914 of FIG. 9.In some embodiments, dual-mode optical network interface circuitry 1026may be capable both of Ethernet protocol communications and ofcommunications according to a second, high-performance protocol. Invarious embodiments, dual-mode optical network interface circuitry 1026may include one or more optical transceiver modules 1027, each of whichmay be capable of transmitting and receiving optical signals over eachof one or more optical channels. The embodiments are not limited in thiscontext.

Coupling MPCM 1016 with a counterpart MPCM of a sled space in a givenrack may cause optical connector 1016A to couple with an opticalconnector comprised in the counterpart MPCM. This may generallyestablish optical connectivity between optical cabling of the sled anddual-mode optical network interface circuitry 1026, via each of a set ofoptical channels 1025. Dual-mode optical network interface circuitry1026 may communicate with the physical resources 1005 of sled 1004 viaelectrical signaling media 1028. In addition to the dimensions of thesleds and arrangement of components on the sleds to provide improvedcooling and enable operation at a relatively higher thermal envelope(e.g., 250W), as described above with reference to FIG. 9, in someembodiments, a sled may include one or more additional features tofacilitate air cooling, such as a heatpipe and/or heat sinks arranged todissipate heat generated by physical resources 1005. It is worthy ofnote that although the example sled 1004 depicted in FIG. 10 does notfeature an expansion connector, any given sled that features the designelements of sled 1004 may also feature an expansion connector accordingto some embodiments. The embodiments are not limited in this context.

FIG. 11 illustrates an example of a data center 1100 that may generallybe representative of one in/for which one or more techniques describedherein may be implemented according to various embodiments. As reflectedin FIG. 11, a physical infrastructure management framework 1150A may beimplemented to facilitate management of a physical infrastructure 1100Aof data center 1100. In various embodiments, one function of physicalinfrastructure management framework 1150A may be to manage automatedmaintenance functions within data center 1100, such as the use ofrobotic maintenance equipment to service computing equipment withinphysical infrastructure 1100A. In some embodiments, physicalinfrastructure 1100A may feature an advanced telemetry system thatperforms telemetry reporting that is sufficiently robust to supportremote automated management of physical infrastructure 1100A. In variousembodiments, telemetry information provided by such an advancedtelemetry system may support features such as failureprediction/prevention capabilities and capacity planning capabilities.In some embodiments, physical infrastructure management framework 1150Amay also be configured to manage authentication of physicalinfrastructure components using hardware attestation techniques. Forexample, robots may verify the authenticity of components beforeinstallation by analyzing information collected from a radio frequencyidentification (RFID) tag associated with each component to beinstalled. The embodiments are not limited in this context.

As shown in FIG. 11, the physical infrastructure 1100A of data center1100 may comprise an optical fabric 1112, which may include a dual-modeoptical switching infrastructure 1114. Optical fabric 1112 and dual-modeoptical switching infrastructure 1114 may be the same as—or similarto—optical fabric 412 of FIG. 4 and dual-mode optical switchinginfrastructure 514 of FIG. 5, respectively, and may providehigh-bandwidth, low-latency, multi-protocol connectivity among sleds ofdata center 1100. As discussed above, with reference to FIG. 1, invarious embodiments, the availability of such connectivity may make itfeasible to disaggregate and dynamically pool resources such asaccelerators, memory, and storage. In some embodiments, for example, oneor more pooled accelerator sleds 1130 may be included among the physicalinfrastructure 1100A of data center 1100, each of which may comprise apool of accelerator resources—such as co-processors and/or FPGAs, forexample—that is globally accessible to other sleds via optical fabric1112 and dual-mode optical switching infrastructure 1114.

In another example, in various embodiments, one or more pooled storagesleds 1132 may be included among the physical infrastructure 1100A ofdata center 1100, each of which may comprise a pool of storage resourcesthat is globally accessible to other sleds via optical fabric 1112 anddual-mode optical switching infrastructure 1114. In some embodiments,such pooled storage sleds 1132 may comprise pools of solid-state storagedevices such as solid-state drives (SSDs). In various embodiments, oneor more high-performance processing sleds 1134 may be included among thephysical infrastructure 1100A of data center 1100. In some embodiments,high-performance processing sleds 1134 may comprise pools ofhigh-performance processors, as well as cooling features that enhanceair cooling to yield a higher thermal envelope of up to 250W or more. Invarious embodiments, any given high-performance processing sled 1134 mayfeature an expansion connector 1117 that can accept a far memoryexpansion sled, such that the far memory that is locally available tothat high-performance processing sled 1134 is disaggregated from theprocessors and near memory comprised on that sled. In some embodiments,such a high-performance processing sled 1134 may be configured with farmemory using an expansion sled that comprises low-latency SSD storage.The optical infrastructure allows for compute resources on one sled toutilize remote accelerator/FPGA, memory, and/or SSD resources that aredisaggregated on a sled located on the same rack or any other rack inthe data center. The remote resources can be located one switch jumpaway or two-switch jumps away in the spine-leaf network architecturedescribed above with reference to FIG. 5. The embodiments are notlimited in this context.

In various embodiments, one or more layers of abstraction may be appliedto the physical resources of physical infrastructure 1100A in order todefine a virtual infrastructure, such as a software-definedinfrastructure 1100B. In some embodiments, virtual computing resources1136 of software-defined infrastructure 1100B may be allocated tosupport the provision of cloud services 1140. In various embodiments,particular sets of virtual computing resources 1136 may be grouped forprovision to cloud services 1140 in the form of SDI services 1138.Examples of cloud services 1140 may include—without limitation—softwareas a service (SaaS) services 1142, platform as a service (PaaS) services1144, and infrastructure as a service (IaaS) services 1146.

In some embodiments, management of software-defined infrastructure 1100Bmay be conducted using a virtual infrastructure management framework1150B. In various embodiments, virtual infrastructure managementframework 1150B may be designed to implement workload fingerprintingtechniques and/or machine-learning techniques in conjunction withmanaging allocation of virtual computing resources 1136 and/or SDIservices 1138 to cloud services 1140. In some embodiments, virtualinfrastructure management framework 1150B may use/consult telemetry datain conjunction with performing such resource allocation. In variousembodiments, an application/service management framework 1150C may beimplemented in order to provide QoS management capabilities for cloudservices 1140. The embodiments are not limited in this context.

Referring now to FIG. 12, a system 1210 for providing acceleratedfunctions as a service may be implemented in accordance with the datacenters 100, 300, 400, 1100 described above with reference to FIGS. 1,3, 4, and 11. In the illustrative embodiment, the system 1210 includesan orchestrator server 1220 communicatively coupled to multiple sledsincluding a compute sled 1230 and accelerator sleds 1240, 1242. One ormore of the sleds 1230, 1240, 1242 may be grouped into a managed node,such as by the orchestrator server 1220, to collectively perform aworkload, such as an application. A managed node may be embodied as anassembly of resources (e.g., physical resources 206), such as computeresources (e.g., physical compute resources 205-4), memory resources(e.g., physical memory resources 205-3), storage resources (e.g.,physical storage resources 205-1), or other resources (e.g., physicalaccelerator resources 205-2), from the same or different sleds (e.g.,the sleds 204-1, 204-2, 204-3, 204-4, etc.) or racks (e.g., one or moreof racks 302-1 through 302-32). Further, a managed node may beestablished, defined, or “spun up” by the orchestrator server 1220 atthe time a workload is to be assigned to the managed node or at anyother time, and may exist regardless of whether any workloads arepresently assigned to the managed node. The system 1210 may be locatedin a data center and provide storage and compute services (e.g., cloudservices) to a client device 1214 that is in communication with thesystem 1210 through a network 1212. The orchestrator server 1220 maysupport a cloud operating environment, such as OpenStack, and managednodes established by the orchestrator server 1220 may execute one ormore applications or processes (i.e., workloads), such as in virtualmachines or containers, on behalf of a user of the client device 1214.

In the illustrative embodiment, the compute sled 1230 includes a centralprocessing unit (CPU) 1232 (e.g., a processor or other device orcircuitry capable of performing a series of operations) that executes aworkload 1234 (e.g., an application). The accelerator sled 1240, in theillustrative embodiment, includes multiple accelerator devices 1260,1262, each of which includes multiple kernels 1270, 1272, 1274, 1276.Each accelerator device 1260, 1262 may be embodied as any device orcircuitry (e.g., a specialized processor, an FPGA, an ASIC, a graphicsprocessing unit (GPU), reconfigurable hardware, etc.) capable ofaccelerating the execution of a function. Each kernel 1270, 1272, 1274,1276 may be embodied as a set of code or a configuration of a portion ofthe corresponding accelerator device 1260, 1262 that causes theaccelerator device 1260, 1262 to perform one or more acceleratedfunctions (e.g., cryptographic operations, compression operations,etc.). Similarly, the accelerator sled 1242, includes acceleratordevices 1264, 1266 and corresponding kernels 1278, 1280, 1282, 1284,similar to the accelerator devices 1260, 1262 and kernels 1270, 1272,1274, 1276. In operation, the orchestrator server 1220 maintains adatabase of which kernels are present on which accelerator sleds (e.g.,on an accelerator device of one of the accelerator sleds 1240, 1242),receives requests to accelerate portions of workloads (e.g., tasks),determines the type of acceleration (e.g., the function(s) to beaccelerated) associated with a task using information in the request,and assigns the task to one or more corresponding accelerator sleds1240, 1242. Furthermore, to provide additional flexibility, theorchestrator server 1220 may coordinate installing and/or removingkernels from the accelerator sleds to accommodate requests foracceleration of tasks from compute sleds (e.g., the compute sled 1230).As such, the system 1210 provides accelerated functions as a service forworkloads, rather than limiting workloads to the accelerationcapabilities of the accelerator devices, if any, that may be local tothe CPU 1232 (e.g., physically located on the compute sled 1230) wherethe workload is executed.

Referring now to FIG. 13, the orchestrator server 1220 may be embodiedas any type of compute device capable of performing the functionsdescribed herein, including receiving a request to accelerate a taskassociated with a kernel (e.g., the kernel 1270) usable by anaccelerator sled (e.g., the accelerator sled 1240) communicativelycoupled to the orchestrator server 1220 to execute the task,determining, in response to the request and with a kernel map databaseindicative of kernels and associated accelerator sleds, an acceleratorsled (e.g., the accelerator sled 1240) that includes an acceleratordevice (e.g., the accelerator devices 1260) configured with the kernelassociated with the request, and assigning the task to the determinedaccelerator sled for execution.

As shown in FIG. 13, the illustrative orchestrator server 1220 includesa compute engine 1302, an input/output (I/O) subsystem 1308,communication circuitry 1310, and one or more data storage devices 1314.Of course, in other embodiments, the orchestrator server 1220 mayinclude other or additional components, such as those commonly found ina computer (e.g., display, peripheral devices, etc.). Additionally, insome embodiments, one or more of the illustrative components may beincorporated in, or otherwise form a portion of, another component.

The compute engine 1302 may be embodied as any type of device orcollection of devices capable of performing various compute functionsdescribed below. In some embodiments, the compute engine 1302 may beembodied as a single device such as an integrated circuit, an embeddedsystem, a field-programmable gate array (FPGA), a system-on-a-chip(SOC), or other integrated system or device. Additionally, in someembodiments, the compute engine 1302 includes or is embodied as aprocessor 1304 and a memory 1306. The processor 1304 may be embodied asany type of processor capable of performing the functions describedherein. For example, the processor 1304 may be embodied as a single ormulti-core processor(s), a microcontroller, or other processor orprocessing/controlling circuit. In some embodiments, the processor 1304may be embodied as, include, or be coupled to an FPGA, an applicationspecific integrated circuit (ASIC), reconfigurable hardware or hardwarecircuitry, or other specialized hardware to facilitate performance ofthe functions described herein. Additionally, in the illustrativeembodiment, the processor 1304 includes a kernel tracker logic unit1320, which may be embodied as any circuitry or device (e.g., an FPGA,an ASIC, a co-processor, etc.) capable of offloading, from the processor1304, the operations described herein associated with providingaccelerated functions as a service.

The main memory 1306 may be embodied as any type of volatile (e.g.,dynamic random access memory (DRAM), etc.) or non-volatile memory ordata storage capable of performing the functions described herein.Volatile memory may be a storage medium that requires power to maintainthe state of data stored by the medium. Non-limiting examples ofvolatile memory may include various types of random access memory (RAM),such as dynamic random access memory (DRAM) or static random accessmemory (SRAM). One particular type of DRAM that may be used in a memorymodule is synchronous dynamic random access memory (SDRAM). Inparticular embodiments, DRAM of a memory component may comply with astandard promulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2Ffor DDR2 SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM,JESD209 for Low Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 forLPDDR3, and JESD209-4 for LPDDR4 (these standards are available atwww.jedec.org). Such standards (and similar standards) may be referredto as DDR-based standards and communication interfaces of the storagedevices that implement such standards may be referred to as DDR-basedinterfaces.

In one embodiment, the memory device is a block addressable memorydevice, such as those based on NAND or NOR technologies. A memory devicemay also include future generation nonvolatile devices, such as a threedimensional crosspoint memory device (e.g., Intel 3D XPoint™ memory), orother byte addressable write-in-place nonvolatile memory devices. In oneembodiment, the memory device may be or may include memory devices thatuse chalcogenide glass, multi-threshold level NAND flash memory, NORflash memory, single or multi-level Phase Change Memory (PCM), aresistive memory, nanowire memory, ferroelectric transistor randomaccess memory (FeTRAM), anti-ferroelectric memory, magnetoresistiverandom access memory (MRAM) memory that incorporates memristortechnology, resistive memory including the metal oxide base, the oxygenvacancy base and the conductive bridge Random Access Memory (CB-RAM), orspin transfer torque (STT)-MRAM, a spintronic magnetic junction memorybased device, a magnetic tunneling junction (MTJ) based device, a DW(Domain Wall) and SOT (Spin Orbit Transfer) based device, a thyristorbased memory device, or a combination of any of the above, or othermemory. The memory device may refer to the die itself and/or to apackaged memory product.

In some embodiments, 3D crosspoint memory (e.g., Intel 3D XPoint™memory) may comprise a transistor-less stackable cross pointarchitecture in which memory cells sit at the intersection of word linesand bit lines and are individually addressable and in which bit storageis based on a change in bulk resistance. In some embodiments, all or aportion of the main memory 1306 may be integrated into the processor1304. In operation, the main memory 1306 may store various software anddata used during operation such as task request data, kernel map data,telemetry data, applications, programs, libraries, and drivers.

The compute engine 1302 is communicatively coupled to other componentsof the orchestrator server 1220 via the I/O subsystem 1308, which may beembodied as circuitry and/or components to facilitate input/outputoperations with the compute engine 1302 (e.g., with the processor 1304and/or the main memory 1306) and other components of the orchestratorserver 1220. For example, the I/O subsystem 1308 may be embodied as, orotherwise include, memory controller hubs, input/output control hubs,integrated sensor hubs, firmware devices, communication links (e.g.,point-to-point links, bus links, wires, cables, light guides, printedcircuit board traces, etc.), and/or other components and subsystems tofacilitate the input/output operations. In some embodiments, the I/Osubsystem 1308 may form a portion of a system-on-a-chip (SoC) and beincorporated, along with one or more of the processor 1304, the mainmemory 1306, and other components of the orchestrator server 1220, intothe compute engine 1302.

The communication circuitry 1310 may be embodied as any communicationcircuit, device, or collection thereof, capable of enablingcommunications over the network 1212 between the orchestrator server1220 and another compute device (e.g., the compute sled 1230, theaccelerator sleds 1240, 1242, etc.). The communication circuitry 1310may be configured to use any one or more communication technology (e.g.,wired or wireless communications) and associated protocols (e.g.,Ethernet, Bluetooth®, Wi-Fi®, WiMAX, etc.) to effect such communication.

The illustrative communication circuitry 1310 includes a networkinterface controller (NIC) 1312, which may also be referred to as a hostfabric interface (HFI). The NIC 1312 may be embodied as one or moreadd-in-boards, daughter cards, network interface cards, controllerchips, chipsets, or other devices that may be used by the orchestratorserver 1220 to connect with another compute device (e.g., the computesled 1230, the accelerator sleds 1240, 1242 etc.). In some embodiments,the NIC 1312 may be embodied as part of a system-on-a-chip (SoC) thatincludes one or more processors, or included on a multichip package thatalso contains one or more processors. In some embodiments, the NIC 1312may include a local processor (not shown) and/or a local memory (notshown) that are both local to the NIC 1312. In such embodiments, thelocal processor of the NIC 1312 may be capable of performing one or moreof the functions of the compute engine 1302 described herein.Additionally or alternatively, in such embodiments, the local memory ofthe NIC 1312 may be integrated into one or more components of theorchestrator server 1220 at the board level, socket level, chip level,and/or other levels.

The one or more illustrative data storage devices 1314, may be embodiedas any type of devices configured for short-term or long-term storage ofdata such as, for example, memory devices and circuits, memory cards,hard disk drives, solid-state drives, or other data storage devices.Each data storage device 1314 may include a system partition that storesdata and firmware code for the data storage device 1314. Each datastorage device 1314 may also include an operating system partition thatstores data files and executables for an operating system.

Additionally or alternatively, the orchestrator server 1220 may includeone or more peripheral devices 1316. Such peripheral devices 1316 mayinclude any type of peripheral device commonly found in a compute devicesuch as a display, speakers, a mouse, a keyboard, and/or otherinput/output devices, interface devices, and/or other peripheraldevices.

Referring now to FIG. 14, the orchestrator server 1220 may establish anenvironment 1400 during operation. The illustrative environment 1400includes a network communicator 1420 and an acceleration service manager1430. Each of the components of the environment 1400 may be embodied ashardware, firmware, software, or a combination thereof. As such, in someembodiments, one or more of the components of the environment 1400 maybe embodied as circuitry or a collection of electrical devices (e.g.,network communicator circuitry 1420, acceleration service managercircuitry 1430, etc.). It should be appreciated that, in suchembodiments, one or more of the network communicator circuitry 1420 oracceleration service manager circuitry 1430 may form a portion of one ormore of the compute engine 1302, the kernel tracker logic unit 1320, thecommunication circuitry 1310, the I/O subsystem 1308, and/or othercomponents of the orchestrator server 1220. In the illustrativeembodiment, the environment 1400 includes task request data 1402, whichmay be embodied as any data indicative of one or more requests receivedby the orchestrator server 1220 from a compute sled (e.g., the computesled 1230) to accelerate a task (e.g., one or more functions) associatedwith all or a portion of a workload. Additionally, in the illustrativeembodiment, the environment 1400 includes kernel map data 1404 which maybe embodied as any data indicative of kernels associated with theaccelerator sleds 1240, 1242. In the illustrative embodiment, a kernelis associated with an accelerator sled if an accelerator device of theaccelerator sled is presently configured with the kernel (e.g., a slotof an FPGA is configured with the kernel). Additionally, in theillustrative embodiment, the environment 1400 includes telemetry data1406, which may be embodied as any data indicative of the performance(e.g., operations per second, a present amount of the totalcomputational capacity of the accelerator device presently being used,etc., referred to herein as a utilization load) and other conditions,such as power usage, of each accelerator device 1260, 1262, 1264, 1266of each accelerator sled 1240, 1242.

In the illustrative environment 1400, the network communicator 1420,which may be embodied as hardware, firmware, software, virtualizedhardware, emulated architecture, and/or a combination thereof asdiscussed above, is configured to facilitate inbound and outboundnetwork communications (e.g., network traffic, network packets, networkflows, etc.) to and from the orchestrator server 1220, respectively. Todo so, the network communicator 1420 is configured to receive andprocess data packets from one system or computing device (e.g., thecompute sled 1230) and to prepare and send data packets to anothercomputing device or system (e.g., the accelerator sleds 1240, 1242).Accordingly, in some embodiments, at least a portion of thefunctionality of the network communicator 1420 may be performed by thecommunication circuitry 1310, and, in the illustrative embodiment, bythe NIC 1312.

The acceleration service manager 1430, which may be embodied ashardware, firmware, software, virtualized hardware, emulatedarchitecture, and/or a combination thereof, is configured to coordinatereceiving a request to accelerate a task, determine one or moreaccelerator sleds 1240, 1242 to perform the task, based on whether theaccelerator sled 1240, 1242 already has the kernel associated with thetask on an accelerator device or has capacity to configure anaccelerator device with the kernel (e.g., in an FPGA slot), and assignthe task to the determined accelerator sled(s) 1240, 1242 for execution.To do so, in the illustrative embodiment, the acceleration servicemanager 1430 includes a task request manager 1432, a kernel manager1434, and a utilization manager 1436. The task request manager 1432, inthe illustrative embodiment, is configured to receive a task request anddetermine characteristics of the task, including the kernel to be usedto accelerate the function(s) of the task, whether the task can beperformed by multiple accelerator devices concurrently (e.g., throughvirtualization, sharing of data through virtualized shared memory,etc.), and/or quality of service targets (e.g., a target latency, atarget throughput, etc.). The kernel manager 1434, in the illustrativeembodiment is configured to determine, using the kernel map data 1404,which accelerator sled 1240, 1242, if any, already has the kernel (e.g.,the accelerator device 1260 of accelerator sled 1240 may already have aslot configured with the kernel). In some embodiments, if the kernel isnot present on an accelerator sled 1240, 1242, the kernel manager 1434coordinates configuring at least one of the accelerator devices of theaccelerator sleds 1240, 1242 with the kernel. The utilization manager1436, in the illustrative embodiment, is configured to collect thetelemetry data 1406 and analyze the telemetry data 1406 to assist indetermining which accelerator sled 1240, 1242 should be selected toaccelerate a task. For example, if multiple accelerator sleds 1240, 1242presently have the kernel associated with a task request, theutilization manager 1436 may analyze the telemetry data 1406 todetermine which accelerator sled 1240, 1242 has enough utilizationcapacity (e.g., the utilization load satisfies a predefined threshold)to meet a quality of service target (e.g., a target latency to completethe task).

Referring now to FIG. 15, the orchestrator server 1220, in operation,may execute a method 1500 to provide accelerated functions as a service.The method 1500 begins with block 1502 in which the orchestrator server1220 determines whether to enable accelerated functions as a service. Inthe illustrative embodiment, the orchestrator server 1220 may determineto enable accelerated functions as a service if the orchestrator server1220 is communicatively coupled to one or more accelerator sleds (e.g.,the accelerator sleds 1240, 1242) and has assigned a workload to acompute sled (the workload 1234 assigned to compute sled 1230). In otherembodiments, the orchestrator server 1220 may determine whether toenable accelerated functions as a service based on other factors.Regardless, in response to a determination to enable acceleratedfunctions as a service, the method 1500 advances to block 1504 in whichthe orchestrator server 1220 may receive a request to accelerate a taskassociated with a kernel (e.g., a task request). In doing so, theorchestrator server 1220 may receive a request from a compute sled (e.g.the compute sled 1230) executing a workload (e.g., the workload 1234),as indicated in block 1506. Further, in receiving the request, theorchestrator server 1220 may receive a request that includes metadataindicative of characteristics and parameters (e.g., input data,settings, etc.), of the task, as indicated in block 1508. Referringbriefly to FIG. 18, the information 1800 indicated in the metadata mayalso include a type of the workload for which the task is to beaccelerated (e.g., a workload that supports a convolutional neuralnetwork, a data compression workload, a data encryption workload, etc.)and characteristics, such as quality of service requirements and/orvirtualization capabilities of the task. Referring back to FIG. 15, inblock 1510, the orchestrator server 1220 may receive a request withmetadata indicative of virtualization capabilities of the task (e.g.,whether the task can be divided into functions that may be performed byseparate virtual machines). In the illustrative embodiment, theorchestrator server 1220 may receive metadata indicative of concurrentexecution capabilities (e.g., whether the functions may be performed atthe same time, such as in separate virtual machines), as indicated inblock 1512. Additionally or alternatively, the orchestrator server 1220may receive a request with metadata indicative of the number ofvirtualizable functions of the task, as indicated in block 1514.

Additionally or alternatively, in receiving the request, theorchestrator server 1220 may receive a request with metadata indicativeof target quality of service data (e.g., pursuant to a service levelagreement (SLA)), as indicated in block 1516. For example, and asindicated in block 1518, the metadata may indicate a target latency(e.g., a maximum number of milliseconds that may elapse before aparticular function is completed). As another example, the metadata mayindicate a target throughput (e.g., a minimum number of operations persecond), as indicated in block 1520. In the illustrative embodiment, therequest identifies the kernel associated with the task, as indicated inblock 1522. As such, and as indicated in block 1524, in someembodiments, the orchestrator server 1220 may receive a request thatincludes the kernel itself, such as in the form of a bitstream, asindicated in block 1526, or executable code embodying the kernel, asindicated in block 1528. In the illustrative embodiment, the requestincludes an identifier of the kernel (e.g., a universally uniqueidentifier (UUID)), as indicated in block 1530. In block 1532, theorchestrator server 1220 determines the subsequent course of actionbased on whether a task request was received. If no task request wasreceived, the method 1500 loops back to block 1502 to determine whetherto continue to enable accelerated functions as a service. Otherwise, themethod 1500 advances to block 1534 of FIG. 16, in which the orchestratorserver 1220 determines whether the kernel associated with the task isalready present in an accelerator sled 1240, 1242.

Referring now to FIG. 16, in determining whether the kernel is alreadypresent in an accelerator sled, the orchestrator server 1220 may comparean identifier of the kernel to a kernel map database (e.g., the kernelmap data 1404) indicative of kernel identifiers and accelerator sleds(e.g., accelerator sleds on which the corresponding kernel is present),as indicated in block 1536. In doing so, the orchestrator server 1220may perform the comparison with a kernel identifier included in the taskrequest (e.g., the kernel identifier from block 1530 of FIG. 15), asindicated in block 1538. Alternatively, the orchestrator server 1220 mayperform the comparison with a hash of the kernel included in the taskrequest (e.g., a hash produced by the orchestrator server 1220 of thebitstream or executable code), as indicated in block 1540. In block1542, the orchestrator server 1220 determines the subsequent course ofaction as a function of whether the kernel is already present in anaccelerator sled 1240, 1242. If not, the method 1500 advances to block1544 in which the orchestrator server 1220 determines an acceleratorsled with capacity to be configured with the kernel. In doing so, theorchestrator server 1220 may request the accelerator sleds 1240, 1242 todetermine whether unused capacity is present, as indicated in block1546. In doing so, and as indicated in block 1548, the orchestratorserver 1220 may query the accelerator sleds 1240, 1242 to determinewhether an unused FPGA slot is present (e.g., if one or more of theaccelerator devices 1260, 1262, 1264, 1266 is an FPGA). As indicated inblock 1550, the orchestrator server 1220 may request an accelerator sled1220 to generate capacity by removing a kernel that does not satisfy athreshold usage level (e.g., the kernel has not been used within apredefined time period). For example, in generating the capacity, thebitstream of the kernel to be removed may be saved in memory, but thegates of the corresponding FPGA slot may be designated for reprogrammingbased on the bitstream for the new kernel. Subsequently, theorchestrator server 1220 sends the kernel (e.g., the bitstream orexecutable code embodying the kernel) to the determined accelerator sledfor configuration (e.g., for programming), as indicated in block 1552.Afterwards, the method 1500 advances to block 1554 in which theorchestrator server 1220 updates the kernel map database (e.g., thekernel map data 1404) to indicate that the kernel is associated with thedetermined accelerator sled. Subsequently, or if the orchestrator server1220 determined in block 1542 that the kernel is already present in anaccelerator sled, the method 1500 advances to block 1556 in which theorchestrator server 1220 receives telemetry data (e.g., the telemetrydata 1406) indicative of utilization loads (e.g., an amount of theavailable acceleration capacity being used) of each accelerator sled1240, 1242. In doing so, in the illustrative embodiment, theorchestrator server 1220 receives telemetry data indicative ofutilization loads of each accelerator device of each accelerator sled1240, 1242. Though described as occurring at a particular location in asequence in the method 1500, it should be understood that orchestratorserver 1220 may receive the telemetry data 1406 at any time, includingin parallel with the other operations performed in the method 1500.

Referring now to FIG. 17, the method 1500 continues to block 1560, inwhich the orchestrator server 1220 selects, to execute the task, anaccelerator sled 1240, 1242 that is configured with the kernel and thathas a utilization load that satisfies a predefined threshold. In doingso, the orchestrator server 1220 may select an accelerator sled 1240,1242 that has a utilization load that satisfies a threshold associatedwith a target quality of service (e.g., a present utilization load ofless than 80% to satisfy a target latency or throughput associated withone quality of service, or a utilization load of less than 60% tosatisfy a target latency or throughput associated with a second qualityof service that is more demanding that the first quality of service), asindicated in block 1562. As indicated in block 1564, the orchestratorserver 1220 may compare the telemetry data 1406 for each acceleratorsled to the predefined threshold. In some embodiments, the orchestratorserver 1220 may make the selection of the accelerator sled as a functionof additional criteria, such as a target power usage, which may beindicated in the task request metadata, in a configuration setting froman administrator of the system 1210, or from another source, asindicated in block 1566. In some embodiments, the orchestrator server1220 may select multiple accelerator sleds 1240, 1242 to execute thetask (e.g., where the task can be divided into multiple virtualizedfunctions across the multiple accelerator devices and acceleratorsleds), as indicated in block 1568.

Subsequently, in block 1570, the orchestrator server 1220 assigns thetask associated with the task request to the selected acceleratorsled(s) for execution. In doing so, the orchestrator server 1220, in theillustrative embodiment, sends an assignment request to the selectedaccelerator sled(s), as indicated in block 1572. As indicated in block1574, the orchestrator server 1220 may send an assignment request thatincludes metadata from the task request (e.g., all or a portion of themetadata received in block 1508 of FIG. 15). In sending the assignmentrequest with the metadata, the orchestrator server 1220 may send anassignment request that includes quality of service target data (e.g.,the quality of service target data received in block 1516 of FIG. 15),as indicated in block 1576. Additionally or alternatively, theorchestrator server 1220 may send an assignment request that includesvirtualization data (e.g., the virtualization capabilities data receivedin block 1510 of FIG. 15), as indicated in block 1578. Further, asindicated in block 1580, the orchestrator server 1220 may send anassignment request that includes concurrent execution data, as indicatedin block 1580, shared virtual memory address data as indicated in block1582, and/or identifiers of multiple accelerator devices and/oraccelerator sleds that are to share data to perform the task (e.g.,through the shared virtual memory address and/or by sending the datadirectly from an accelerator device and/or accelerator sled to another).Subsequently, the method 1500 loops back to block 1502 of FIG. 15 inwhich the orchestrator server 1220 determines whether to continue toenable accelerated functions as a service.

EXAMPLES

Illustrative examples of the technologies disclosed herein are providedbelow. An embodiment of the technologies may include any one or more,and any combination of, the examples described below.

Example 1 includes a compute device comprising a compute engine toreceive a request for an accelerated task, wherein the task isassociated with a kernel usable by an accelerator sled communicativelycoupled to the compute device to execute the task; determine, inresponse to the request and with a database indicative of kernels andassociated accelerator sleds, an accelerator sled that includes anaccelerator device configured with the kernel associated with therequest; and assign the task to the determined accelerator sled forexecution.

Example 2 includes the subject matter of Example 1, and wherein todetermine an accelerator sled that includes an accelerator deviceconfigured with the kernel comprises to determine that an acceleratorsled is not presently associated with the kernel; determine anaccelerator sled with capacity to be configured with the kernel; sendthe kernel to the determined accelerator sled for configuration; andupdate the database to indicate that the kernel is associated with thedetermined accelerator sled.

Example 3 includes the subject matter of any of Examples 1 and 2, andwherein to determine an accelerator sled with capacity to be configuredwith the kernel comprises to determine a field programmable gate array(FPGA) with an unused slot to be configured with the kernel.

Example 4 includes the subject matter of any of Examples 1-3, andwherein to determine an accelerator sled that includes an acceleratordevice configured with the kernel comprises to determine multipleaccelerator sleds that each include an accelerator device configuredwith the kernel; and wherein the compute engine is further to select anaccelerator sled that is configured with the kernel and that has autilization load that satisfies a predefined threshold to execute thetask; and wherein to assign the task to the determined accelerator sledcomprises to assign the task to the selected accelerator sled.

Example 5 includes the subject matter of any of Examples 1-4, andwherein the compute device is communicatively coupled to the multipleaccelerator sleds and the compute engine is further to receive, fromeach accelerator sled, data indicative of a utilization load associatedwith each accelerator sled; and wherein to select an accelerator sledthat is configured with the kernel and that has a utilization load thatsatisfies a predefined threshold to execute the task comprises tocompare the data received from each accelerator sled to the predefinedthreshold.

Example 6 includes the subject matter of any of Examples 1-5, andwherein to receive a request for an accelerated task comprises toreceive a request that includes metadata indicative of characteristicsand parameters of the task.

Example 7 includes the subject matter of any of Examples 1-6, andwherein to receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of a target quality of serviceassociated with the task; wherein to determine an accelerator sled thatincludes an accelerator device configured with the kernel comprises todetermine multiple accelerator sleds that each include an acceleratordevice configured with the kernel; and wherein the compute engine isfurther to select an accelerator sled that is configured with the kerneland that has a utilization load that satisfies a predefined thresholdassociated with the target quality of service to execute the task.

Example 8 includes the subject matter of any of Examples 1-7, andwherein to receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of virtualization capabilitiesof the task.

Example 9 includes the subject matter of any of Examples 1-8, andwherein to receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of concurrent executioncapabilities of the task; and wherein to assign the task comprises toassign the task to multiple accelerator sleds for concurrent execution.

Example 10 includes the subject matter of any of Examples 1-9, andwherein to assign the task to multiple accelerator sleds for concurrentexecution comprises to send an assignment request to the multipleaccelerator sleds, wherein the assignment request includes identifiersof the multiple accelerator sleds assigned to the task to enable data tobe shared among the assigned accelerator sleds as the task isconcurrently executed.

Example 11 includes the subject matter of any of Examples 1-10, andwherein to assign the task to multiple accelerator sleds for concurrentexecution comprises to send an assignment request to the multipleaccelerator sleds, wherein the assignment request includes sharedvirtual memory address data usable by the multiple accelerator sleds toshare data in virtual memory as the task is concurrently executed.

Example 12 includes the subject matter of any of Examples 1-11, andwherein to receive the request comprises to receive a request thatincludes an identifier of the kernel; and wherein to determine, inresponse to the request and with a database indicative of kernels andassociated accelerator sleds, an accelerator sled that includes anaccelerator device configured with the kernel associated with therequest comprises to compare the received identifier to kernelidentifiers in the database.

Example 13 includes the subject matter of any of Examples 1-12, andwherein to receive the request comprises to receive a request thatincludes the kernel; and wherein to determine, in response to therequest and with a database indicative of kernels and associatedaccelerator sleds, an accelerator sled that includes an acceleratordevice configured with the kernel associated with the request comprisesto obtain a hash of the received kernel; and compare the hash to kernelidentifiers in the database.

Example 14 includes the subject matter of any of Examples 1-13, andwherein to receive the request comprises to receive the request from acompute sled executing a workload associated with the task.

Example 15 includes a method comprising receiving, by a compute device,a request for an accelerated task, wherein the task is associated with akernel usable by an accelerator sled communicatively coupled to thecompute device to execute the task; determining, by the compute deviceand in response to the request and with a database indicative of kernelsand associated accelerator sleds, an accelerator sled that includes anaccelerator device configured with the kernel associated with therequest; and assigning, by the compute device, the task to thedetermined accelerator sled for execution.

Example 16 includes the subject matter of Example 15, and whereindetermining an accelerator sled that includes an accelerator deviceconfigured with the kernel comprises determining that an acceleratorsled is not presently associated with the kernel; determining anaccelerator sled with capacity to be configured with the kernel; sendingthe kernel to the determined accelerator sled for configuration; andupdating the database to indicate that the kernel is associated with thedetermined accelerator sled.

Example 17 includes the subject matter of any of Examples 15 and 16, andwherein determining an accelerator sled with capacity to be configuredwith the kernel comprises determining a field programmable gate array(FPGA) with an unused slot to be configured with the kernel.

Example 18 includes the subject matter of any of Examples 15-17, andwherein determining an accelerator sled that includes an acceleratordevice configured with the kernel comprises determining multipleaccelerator sleds that each include an accelerator device configuredwith the kernel; and the method further comprising selecting, by thecompute device, an accelerator sled that is configured with the kerneland that has a utilization load that satisfies a predefined threshold toexecute the task; and wherein assigning the task to the determinedaccelerator sled comprises assigning the task to the selectedaccelerator sled.

Example 19 includes the subject matter of any of Examples 15-18, andwherein the compute device is communicatively coupled to the multipleaccelerator sleds, the method further comprising receiving, by thecompute device and from each accelerator sled, data indicative of autilization load associated with each accelerator sled; and whereinselecting an accelerator sled that is configured with the kernel andthat has a utilization load that satisfies a predefined threshold toexecute the task comprises comparing the data received from eachaccelerator sled to the predefined threshold.

Example 20 includes the subject matter of any of Examples 15-19, andwherein receiving a request for an accelerated task comprises receivinga request that includes metadata indicative of characteristics andparameters of the task.

Example 21 includes the subject matter of any of Examples 15-20, andwherein receiving a request that includes metadata indicative ofcharacteristics and parameters of the task comprises receiving a requestthat includes metadata indicative of a target quality of serviceassociated with the task; and wherein determining an accelerator sledthat includes an accelerator device configured with the kernel comprisesdetermining multiple accelerator sleds that each include an acceleratordevice configured with the kernel; and the method further comprisingselecting, by the compute device, an accelerator sled that is configuredwith the kernel and that has a utilization load that satisfies apredefined threshold associated with the target quality of service toexecute the task.

Example 22 includes the subject matter of any of Examples 15-21, andwherein receiving a request that includes metadata indicative ofcharacteristics and parameters of the task comprises receiving a requestthat includes metadata indicative of virtualization capabilities of thetask.

Example 23 includes the subject matter of any of Examples 15-22, andwherein receiving a request that includes metadata indicative ofcharacteristics and parameters of the task comprises receiving a requestthat includes metadata indicative of concurrent execution capabilitiesof the task; and wherein assigning the task comprises assigning the taskto multiple accelerator sleds for concurrent execution.

Example 24 includes the subject matter of any of Examples 15-23, andwherein assigning the task to multiple accelerator sleds for concurrentexecution comprises sending an assignment request to the multipleaccelerator sleds, wherein the assignment request includes identifiersof the multiple accelerator sleds assigned to the task to enable data tobe shared among the assigned accelerator sleds as the task isconcurrently executed.

Example 25 includes the subject matter of any of Examples 15-24, andwherein assigning the task to multiple accelerator sleds for concurrentexecution comprises sending an assignment request to the multipleaccelerator sleds, wherein the assignment request includes sharedvirtual memory address data usable by the multiple accelerator sleds toshare data in virtual memory as the task is concurrently executed.

Example 26 includes the subject matter of any of Examples 15-25, andwherein receiving the request comprises receiving a request thatincludes an identifier of the kernel; and wherein determining, inresponse to the request and with a database indicative of kernels andassociated accelerator sleds, an accelerator sled that includes anaccelerator device configured with the kernel associated with therequest comprises comparing the received identifier to kernelidentifiers in the database.

Example 27 includes the subject matter of any of Examples 15-26, andwherein receiving the request comprises receiving a request thatincludes the kernel; and wherein determining, in response to the requestand with a database indicative of kernels and associated acceleratorsleds, an accelerator sled that includes an accelerator deviceconfigured with the kernel associated with the request comprisesobtaining a hash of the received kernel; and comparing the hash tokernel identifiers in the database.

Example 28 includes the subject matter of any of Examples 15-27, andwherein receiving the request comprises receiving the request from acompute sled executing a workload associated with the task.

Example 29 includes one or more machine-readable storage mediacomprising a plurality of instructions stored thereon that, in responseto being executed, cause a compute device to perform the method of anyof Examples 15-28.

Example 30 includes a compute device comprising means for performing themethod of any of Examples 15-28.

Example 31 includes a compute device comprising one or more processors;one or more memory devices having stored therein a plurality ofinstructions that, when executed by the one or more processors, causethe network switch to perform the method of any of Examples 15-28.

Example 32 includes a compute device comprising network communicatorcircuitry to receive a request for an accelerated task, wherein the taskis associated with a kernel usable by an accelerator sledcommunicatively coupled to the compute device to execute the task; andacceleration service manager circuitry to determine, in response to therequest and with a database indicative of kernels and associatedaccelerator sleds, an accelerator sled that includes an acceleratordevice configured with the kernel associated with the request; andassign the task to the determined accelerator sled for execution.

Example 33 includes the subject matter of Example 32, and wherein todetermine an accelerator sled that includes an accelerator deviceconfigured with the kernel comprises to determine that an acceleratorsled is not presently associated with the kernel; determine anaccelerator sled with capacity to be configured with the kernel; sendthe kernel to the determined accelerator sled for configuration; andupdate the database to indicate that the kernel is associated with thedetermined accelerator sled.

Example 34 includes the subject matter of any of Examples 32 and 33, andwherein to determine an accelerator sled with capacity to be configuredwith the kernel comprises to determine a field programmable gate array(FPGA) with an unused slot to be configured with the kernel.

Example 35 includes the subject matter of any of Examples 32-34, andwherein to determine an accelerator sled that includes an acceleratordevice configured with the kernel comprises to determine multipleaccelerator sleds that each include an accelerator device configuredwith the kernel; and wherein the acceleration service manager circuitryis further to select an accelerator sled that is configured with thekernel and that has a utilization load that satisfies a predefinedthreshold to execute the task; and wherein to assign the task to thedetermined accelerator sled comprises to assign the task to the selectedaccelerator sled.

Example 36 includes the subject matter of any of Examples 32-35, andwherein the compute device is communicatively coupled to the multipleaccelerator sleds and the acceleration service manager circuitry isfurther to receive, from each accelerator sled, data indicative of autilization load associated with each accelerator sled; and wherein toselect an accelerator sled that is configured with the kernel and thathas a utilization load that satisfies a predefined threshold to executethe task comprises to compare the data received from each acceleratorsled to the predefined threshold.

Example 37 includes the subject matter of any of Examples 32-36, andwherein to receive a request for an accelerated task comprises toreceive a request that includes metadata indicative of characteristicsand parameters of the task.

Example 38 includes the subject matter of any of Examples 32-37, andwherein to receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of a target quality of serviceassociated with the task; wherein to determine an accelerator sled thatincludes an accelerator device configured with the kernel comprises todetermine multiple accelerator sleds that each include an acceleratordevice configured with the kernel; and wherein the acceleration servicemanager circuitry is further to select an accelerator sled that isconfigured with the kernel and that has a utilization load thatsatisfies a predefined threshold associated with the target quality ofservice to execute the task.

Example 39 includes the subject matter of any of Examples 32-38, andwherein to receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of virtualization capabilitiesof the task.

Example 40 includes the subject matter of any of Examples 32-39, andwherein to receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of concurrent executioncapabilities of the task; and wherein to assign the task comprises toassign the task to multiple accelerator sleds for concurrent execution.

Example 41 includes the subject matter of any of Examples 32-40, andwherein to assign the task to multiple accelerator sleds for concurrentexecution comprises to send an assignment request to the multipleaccelerator sleds, wherein the assignment request includes identifiersof the multiple accelerator sleds assigned to the task to enable data tobe shared among the assigned accelerator sleds as the task isconcurrently executed.

Example 42 includes the subject matter of any of Examples 32-41, andwherein to assign the task to multiple accelerator sleds for concurrentexecution comprises to send an assignment request to the multipleaccelerator sleds, wherein the assignment request includes sharedvirtual memory address data usable by the multiple accelerator sleds toshare data in virtual memory as the task is concurrently executed.

Example 43 includes the subject matter of any of Examples 32-42, andwherein to receive the request comprises to receive a request thatincludes an identifier of the kernel; and wherein to determine, inresponse to the request and with a database indicative of kernels andassociated accelerator sleds, an accelerator sled that includes anaccelerator device configured with the kernel associated with therequest comprises to compare the received identifier to kernelidentifiers in the database.

Example 44 includes the subject matter of any of Examples 32-43, andwherein to receive the request comprises to receive a request thatincludes the kernel; and wherein to determine, in response to therequest and with a database indicative of kernels and associatedaccelerator sleds, an accelerator sled that includes an acceleratordevice configured with the kernel associated with the request comprisesto obtain a hash of the received kernel; and compare the hash to kernelidentifiers in the database.

Example 45 includes the subject matter of any of Examples 32-44, andwherein to receive the request comprises to receive the request from acompute sled executing a workload associated with the task.

Example 46 includes a compute device comprising circuitry for receivinga request for an accelerated task, wherein the task is associated with akernel usable by an accelerator sled communicatively coupled to thecompute device to execute the task; means for determining, in responseto the request and with a database indicative of kernels and associatedaccelerator sleds, an accelerator sled that includes an acceleratordevice configured with the kernel associated with the request; andcircuitry for assigning, by the compute device, the task to thedetermined accelerator sled for execution.

Example 47 includes the subject matter of Example 46, and wherein themeans for determining an accelerator sled that includes an acceleratordevice configured with the kernel comprises circuitry for determiningthat an accelerator sled is not presently associated with the kernel;circuitry for determining an accelerator sled with capacity to beconfigured with the kernel; circuitry for sending the kernel to thedetermined accelerator sled for configuration; and circuitry forupdating the database to indicate that the kernel is associated with thedetermined accelerator sled.

Example 48 includes the subject matter of any of Examples 46 and 47, andwherein the circuitry for determining an accelerator sled with capacityto be configured with the kernel comprises circuitry for determining afield programmable gate array (FPGA) with an unused slot to beconfigured with the kernel.

Example 49 includes the subject matter of any of Examples 46-48, andwherein the means for determining an accelerator sled that includes anaccelerator device configured with the kernel comprises circuitry fordetermining multiple accelerator sleds that each include an acceleratordevice configured with the kernel; and the compute device furthercomprising circuitry for selecting an accelerator sled that isconfigured with the kernel and that has a utilization load thatsatisfies a predefined threshold to execute the task; and wherein thecircuitry for assigning the task to the determined accelerator sledcomprises circuitry for assigning the task to the selected acceleratorsled.

Example 50 includes the subject matter of any of Examples 46-49, andwherein the compute device is communicatively coupled to the multipleaccelerator sleds, the compute device further comprising circuitry forreceiving, from each accelerator sled, data indicative of a utilizationload associated with each accelerator sled; and wherein the means forselecting an accelerator sled that is configured with the kernel andthat has a utilization load that satisfies a predefined threshold toexecute the task comprises circuitry for comparing the data receivedfrom each accelerator sled to the predefined threshold.

Example 51 includes the subject matter of any of Examples 46-50, andwherein the circuitry for receiving a request for an accelerated taskcomprises circuitry for receiving a request that includes metadataindicative of characteristics and parameters of the task.

Example 52 includes the subject matter of any of Examples 46-51, andwherein the circuitry for receiving a request that includes metadataindicative of characteristics and parameters of the task comprisescircuitry for receiving a request that includes metadata indicative of atarget quality of service associated with the task; and wherein themeans for determining an accelerator sled that includes an acceleratordevice configured with the kernel comprises circuitry for determiningmultiple accelerator sleds that each include an accelerator deviceconfigured with the kernel; and the compute device further comprisingcircuitry for selecting an accelerator sled that is configured with thekernel and that has a utilization load that satisfies a predefinedthreshold associated with the target quality of service to execute thetask.

Example 53 includes the subject matter of any of Examples 46-52, andwherein the circuitry for receiving a request that includes metadataindicative of characteristics and parameters of the task comprisescircuitry for receiving a request that includes metadata indicative ofvirtualization capabilities of the task.

Example 54 includes the subject matter of any of Examples 46-53, andwherein the circuitry for receiving a request that includes metadataindicative of characteristics and parameters of the task comprisescircuitry for receiving a request that includes metadata indicative ofconcurrent execution capabilities of the task; and wherein the circuitryfor assigning the task comprises circuitry for assigning the task tomultiple accelerator sleds for concurrent execution.

Example 55 includes the subject matter of any of Examples 46-54, andwherein the circuitry for assigning the task to multiple acceleratorsleds for concurrent execution comprises circuitry for sending anassignment request to the multiple accelerator sleds, wherein theassignment request includes identifiers of the multiple acceleratorsleds assigned to the task to enable data to be shared among theassigned accelerator sleds as the task is concurrently executed.

Example 56 includes the subject matter of any of Examples 46-55, andwherein the circuitry for assigning the task to multiple acceleratorsleds for concurrent execution comprises sending an assignment requestto the multiple accelerator sleds, wherein the assignment requestincludes shared virtual memory address data usable by the multipleaccelerator sleds to share data in virtual memory as the task isconcurrently executed.

Example 57 includes the subject matter of any of Examples 46-56, andwherein the circuitry for receiving the request comprises circuitry forreceiving a request that includes an identifier of the kernel; andwherein the means for determining, in response to the request and with adatabase indicative of kernels and associated accelerator sleds, anaccelerator sled that includes an accelerator device configured with thekernel associated with the request comprises circuitry for comparing thereceived identifier to kernel identifiers in the database.

Example 58 includes the subject matter of any of Examples 46-57, andwherein the circuitry for receiving the request comprises circuitry forreceiving a request that includes the kernel; and wherein the means fordetermining, in response to the request and with a database indicativeof kernels and associated accelerator sleds, an accelerator sled thatincludes an accelerator device configured with the kernel associatedwith the request comprises circuitry for obtaining a hash of thereceived kernel; and circuitry for comparing the hash to kernelidentifiers in the database.

Example 59 includes the subject matter of any of Examples 46-58, andwherein the circuitry for receiving the request comprises circuitry forreceiving the request from a compute sled executing a workloadassociated with the task.

The invention claimed is:
 1. A compute device comprising: a computeengine to: receive a request for an accelerated task, wherein the taskis associated with a kernel usable by an accelerator sledcommunicatively coupled to the compute device to execute the task;determine, in response to the request and with a database indicative ofkernels and associated accelerator sleds, an accelerator sled thatincludes an accelerator device configured with the kernel associatedwith the request; and assign the task to the determined accelerator sledfor execution, wherein determining an accelerator sled that includes anaccelerator device configured with the kernel comprises: requesting thatthe kernel be executed by the determined accelerator sled; and updatingthe database to indicate that the kernel is associated with thedetermined accelerator sled.
 2. The compute device of claim 1, whereinto determine an accelerator sled that includes an accelerator deviceconfigured with the kernel comprises to: determine that an acceleratorsled is not presently associated with the kernel; determine anaccelerator sled with capacity to be configured with the kernel; andsend the kernel to the determined accelerator sled for configuration. 3.The compute device of claim 2, wherein to determine an accelerator sledwith capacity to be configured with the kernel comprises to determine afield programmable gate array (FPGA) with an unused slot to beconfigured with the kernel.
 4. The compute device of claim 1, wherein todetermine an accelerator sled that includes an accelerator deviceconfigured with the kernel comprises to determine multiple acceleratorsleds that each include an accelerator device configured with thekernel; and wherein the compute engine is further to select anaccelerator sled that is configured with the kernel and that has autilization load that satisfies a predefined threshold to execute thetask; and wherein to assign the task to the determined accelerator sledcomprises to assign the task to the selected accelerator sled.
 5. Thecompute device of claim 4, wherein the compute device is communicativelycoupled to the multiple accelerator sleds and the compute engine isfurther to: receive, from each accelerator sled, data indicative of autilization load associated with each accelerator sled; and wherein toselect an accelerator sled that is configured with the kernel and thathas a utilization load that satisfies a predefined threshold to executethe task comprises to compare the data received from each acceleratorsled to the predefined threshold.
 6. The compute device of claim 1,wherein to receive a request for an accelerated task comprises toreceive a request that includes metadata indicative of characteristicsand parameters of the task.
 7. The compute device of claim 6, wherein toreceive a request that includes metadata indicative of characteristicsand parameters of the task comprises to receive a request that includesmetadata indicative of a target quality of service associated with thetask; wherein to determine an accelerator sled that includes anaccelerator device configured with the kernel comprises to determinemultiple accelerator sleds that each include an accelerator deviceconfigured with the kernel; and wherein the compute engine is further toselect an accelerator sled that is configured with the kernel and thathas a utilization load that satisfies a predefined threshold associatedwith the target quality of service to execute the task.
 8. The computedevice of claim 6, wherein to receive a request that includes metadataindicative of characteristics and parameters of the task comprises toreceive a request that includes metadata indicative of virtualizationcapabilities of the task.
 9. The compute device of claim 6, wherein toreceive a request that includes metadata indicative of characteristicsand parameters of the task comprises to receive a request that includesmetadata indicative of concurrent execution capabilities of the task;and wherein to assign the task comprises to assign the task to multipleaccelerator sleds for concurrent execution.
 10. The compute device ofclaim 9, wherein to assign the task to multiple accelerator sleds forconcurrent execution comprises to send an assignment request to themultiple accelerator sleds, wherein the assignment request includesidentifiers of the multiple accelerator sleds assigned to the task toenable data to be shared among the assigned accelerator sleds as thetask is concurrently executed.
 11. The compute device of claim 9,wherein to assign the task to multiple accelerator sleds for concurrentexecution comprises to send an assignment request to the multipleaccelerator sleds, wherein the assignment request includes sharedvirtual memory address data usable by the multiple accelerator sleds toshare data in virtual memory as the task is concurrently executed. 12.The compute device of claim 1, wherein to receive the request comprisesto receive a request that includes an identifier of the kernel; andwherein to determine, in response to the request and with a databaseindicative of kernels and associated accelerator sleds, an acceleratorsled that includes an accelerator device configured with the kernelassociated with the request comprises to compare the received identifierto kernel identifiers in the database.
 13. One or more non-transitorymachine-readable storage media comprising a plurality of instructionsstored thereon that, in response to being executed, cause a computedevice to: receive a request for an accelerated task, wherein the taskis associated with a kernel usable by an accelerator sledcommunicatively coupled to the compute device to execute the task;determine, in response to the request and with a database indicative ofkernels and associated accelerator sleds, an accelerator sled thatincludes an accelerator device configured with the kernel associatedwith the request; and assign the task to the determined accelerator sledfor execution, wherein determining an accelerator sled that includes anaccelerator device configured with the kernel comprises: requesting thatthe kernel be executed by the determined accelerator sled; and updatingthe database to indicate that the kernel is associated with thedetermined accelerator sled.
 14. The one or more non-transitorymachine-readable storage media of claim 13, wherein to determine anaccelerator sled that includes an accelerator device configured with thekernel comprises to: determine that an accelerator sled is not presentlyassociated with the kernel; determine an accelerator sled with capacityto be configured with the kernel; and send the kernel to the determinedaccelerator sled for configuration.
 15. The one or more non-transitorymachine-readable storage media of claim 14, wherein to determine anaccelerator sled with capacity to be configured with the kernelcomprises to determine a field programmable gate array (FPGA) with anunused slot to be configured with the kernel.
 16. The one or morenon-transitory machine-readable storage media of claim 13, wherein todetermine an accelerator sled that includes an accelerator deviceconfigured with the kernel comprises to determine multiple acceleratorsleds that each include an accelerator device configured with thekernel; and wherein the plurality of instructions, when executed,further cause the compute device to select an accelerator sled that isconfigured with the kernel and that has a utilization load thatsatisfies a predefined threshold to execute the task; and wherein toassign the task to the determined accelerator sled comprises to assignthe task to the selected accelerator sled.
 17. The one or morenon-transitory machine-readable storage media of claim 16, wherein thecompute device is communicatively coupled to the multiple acceleratorsleds and the plurality of instructions, when executed, further causethe compute device to: receive, from each accelerator sled, dataindicative of a utilization load associated with each accelerator sled;and wherein to select an accelerator sled that is configured with thekernel and that has a utilization load that satisfies a predefinedthreshold to execute the task comprises to compare the data receivedfrom each accelerator sled to the predefined threshold.
 18. The one ormore non-transitory machine-readable storage media of claim 13, whereinto receive a request for an accelerated task comprises to receive arequest that includes metadata indicative of characteristics andparameters of the task.
 19. The one or more non-transitorymachine-readable storage media of claim 18, wherein to receive a requestthat includes metadata indicative of characteristics and parameters ofthe task comprises to receive a request that includes metadataindicative of a target quality of service associated with the task;wherein to determine an accelerator sled that includes an acceleratordevice configured with the kernel comprises to determine multipleaccelerator sleds that each include an accelerator device configuredwith the kernel; and wherein the plurality of instructions, whenexecuted, further cause the compute device to select an accelerator sledthat is configured with the kernel and that has a utilization load thatsatisfies a predefined threshold associated with the target quality ofservice to execute the task.
 20. The one or more non-transitorymachine-readable storage media of claim 18, wherein to receive a requestthat includes metadata indicative of characteristics and parameters ofthe task comprises to receive a request that includes metadataindicative of virtualization capabilities of the task.
 21. The one ormore non-transitory machine-readable storage media of claim 18, whereinto receive a request that includes metadata indicative ofcharacteristics and parameters of the task comprises to receive arequest that includes metadata indicative of concurrent executioncapabilities of the task; and wherein to assign the task comprises toassign the task to multiple accelerator sleds for concurrent execution.22. The one or more non-transitory machine-readable storage media ofclaim 21, wherein to assign the task to multiple accelerator sleds forconcurrent execution comprises to send an assignment request to themultiple accelerator sleds, wherein the assignment request includesidentifiers of the multiple accelerator sleds assigned to the task toenable data to be shared among the assigned accelerator sleds as thetask is concurrently executed.
 23. The one or more non-transitorymachine-readable storage media of claim 21, wherein to assign the taskto multiple accelerator sleds for concurrent execution comprises to sendan assignment request to the multiple accelerator sleds, wherein theassignment request includes shared virtual memory address data usable bythe multiple accelerator sleds to share data in virtual memory as thetask is concurrently executed.
 24. The one or more non-transitorymachine-readable storage media of claim 13, wherein to receive therequest comprises to receive a request that includes an identifier ofthe kernel; and wherein to determine, in response to the request andwith a database indicative of kernels and associated accelerator sleds,an accelerator sled that includes an accelerator device configured withthe kernel associated with the request comprises to compare the receivedidentifier to kernel identifiers in the database.
 25. A methodcomprising: receiving, by a compute device, a request for an acceleratedtask, wherein the task is associated with a kernel usable by anaccelerator sled communicatively coupled to the compute device toexecute the task; determining, by the compute device and in response tothe request and with a database indicative of kernels and associatedaccelerator sleds, an accelerator sled that includes an acceleratordevice configured with the kernel associated with the request; andassigning, by the compute device, the task to the determined acceleratorsled for execution, wherein determining an accelerator sled thatincludes an accelerator device configured with the kernel comprises:requesting that the kernel be executed by the determined acceleratorsled; and updating the database to indicate that the kernel isassociated with the determined accelerator sled.
 26. The method of claim25, wherein determining an accelerator sled that includes an acceleratordevice configured with the kernel comprises: determining that anaccelerator sled is not presently associated with the kernel;determining an accelerator sled with capacity to be configured with thekernel; and sending the kernel to the determined accelerator sled forconfiguration.
 27. The method of claim 26, wherein determining anaccelerator sled with capacity to be configured with the kernelcomprises determining a field programmable gate array (FPGA) with anunused slot to be configured with the kernel.
 28. The method of claim25, wherein determining an accelerator sled that includes an acceleratordevice configured with the kernel comprises determining multipleaccelerator sleds that each include an accelerator device configuredwith the kernel; and the method further comprising selecting, by thecompute device, an accelerator sled that is configured with the kerneland that has a utilization load that satisfies a predefined threshold toexecute the task; and wherein assigning the task to the determinedaccelerator sled comprises assigning the task to the selectedaccelerator sled.